Social efficiency in microfinance institutions

Microfinance institutions (MFIs) are a special case in the financial world. They have a double financial and social role and need to be efficient at both. In this paper, we try to measure the efficiency of MFIs in relation to financial and social outputs using data envelopment analysis. For the analysis of financial efficiency, we rely on existing literature for traditional financial institutions. To this we have added two indicators of social performance: impact on women and a poverty reach index. We have studied the relationship between social and financial efficiency, and the relationship between efficiency and other indicators, such as profitability. Other aspects studied are the relation between social efficiency and type of institution—Non-Governmental Organization (NGO)—, non-NGO, and the importance of geographical region of activity. The results reveal the importance of social efficiency assessment.     

A Transportation model with multiple criteria and multiple constraint levels

In this paper, a transportation model with multiple criteria and multiple constraint levels (MC²) is formulated by using the framework of MC² linear programming. An algorithm is developed to solve such MC² transportation problems. In this algorithm, the traditional northwest corner rule is adopted to find an initial basic feasible solution for a given MC² transportation problem. Then the MC²-simplex method is applied to locate the set of all potential solutions over possible changes of the objective coefficient parameter and the supply and demand parameter for the MC² transportation problem. A numerical example is illustrated to demonstrate the applicability of the algorithm in solving the MC² transportation problems.     

Two-stage financial risk tolerance assessment using data envelopment analysis

Typical questionnaires administered by financial advisors to assess financial risk tolerance mostly contain stereotypes of people, have seemingly unscientific scoring approaches and often treat risk as a one-dimensional concept. In this work, a mathematical tool was developed to assess relative risk tolerance using Data Envelopment Analysis (DEA). At its core, it is a novel questionnaire that characterizes risk by its four distinct elements: propensity, attitude, capacity, and knowledge. Over 180 individuals were surveyed and their responses were analyzed using the Slacks-based measure type of DEA efficiency model. Results show that the multidimensionality of risk must be considered for complete assessment of risk tolerance. This approach also provides insight into the relationship between risk, its elements and other variables. Specifically, the perception of risk varies by gender as men are generally less risk averse than women. In fact, risk attitude and knowledge scores are consistently lower for women, while there is no statistical difference in their risk capacity and propensity compared to men. The tool can also serve as a “risk calculator” for an appropriate and defensible method to meet legal compliance requirements, known as the “Know Your Client” rule, that exist for Canadian financial institutions and their advisors.     

Efficiency in microfinance: financial and social performance of agricultural credit cooperatives in Bulgaria

Efficiency in microfinance requires accounting for a specific ambiguous production goal. Beyond financial performance, microfinance institutions are to be assessed with regard to their social impact. Based on a comprehensive data collection campaign on 15 Bulgarian agricultural credit cooperatives (ACCs), we compiled a database uniting financial (perennial data from 2000 to 2009) and social performance indicators (2009). The social performance assessment follows an internationally renowned methodology of social auditing. It provides the database for the construction of a social output, which, apart from outreach, evaluates social network quality, client benefits (protection from over-indebtedness) and other items that have not been assessed in efficiency analysis before. We develop a new analytical model for Data Envelopment Analysis and gain meaningful results for the sample of ACCs. Interestingly, the efficiency rankings revealed that only ACCs with sound financial performance can achieve a higher ranking in the specification including the social output.     

DEA meets Picasso: The impact of auction houses on the hammer price

Employing a hedonic price approach within a framework of central tendencies no conclusive results about the impact of auction houses on final prices of art objects have been found. In order to focus on auction houses as a unit we have applied a benchmarking technique, DEA, developed for efficiency studies. New performance indicators are developed and calculated giving an insight into auction house differences impossible to obtain using hedonic price approach. The performance indicators may also be regarded as quality indicators assuming perfect arbitrage leads to the same unobservable quality of art object obtaining the same price.     

Network DEA approach to airports performance assessment considering undesirable outputs

In this paper, a directional distance approach is proposed to deal with network DEA problems in which the processes may generate not only desirable final outputs but also undesirable outputs. The proposed approach is applied to the problem of modelling and benchmarking airport operations. The corresponding network DEA model considers two process (Aircraft Movement and Aircraft Loading) with two final outputs (Annual Passenger Movement and Annual Cargo handled), one intermediate product (Aircraft Traffic Movements) and two undesirable outputs (Number of Delayed Flights and Accumulated Flight Delays). The proposed approach has been applied to Spanish airports data for year 2008 comparing the computed directional distance efficiency scores with those obtained using a conventional, single-process directional distance function approach. From this comparison, it can be concluded that the proposed network DEA approach has more discriminatory power than its single-process counterpart, uncovering more inefficiencies and providing more valid results.     

Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application

Benchmarking is a widely cited method to identify and adopt best-practices as a means to improve performance. Data envelopment analysis (DEA) has been demonstrated to be a powerful benchmarking methodology for situations where multiple inputs and outputs need to be assessed to identify best-practices and improve productivity in organizations. Most DEA benchmarking studies have excluded quality, even in service-sector applications such as health care where quality is a key element of performance. This limits the practical value of DEA in organizations where maintaining and improving service quality is critical to achieving performance objectives. In this paper, alternative methods incorporating quality in DEA benchmarking are demonstrated and evaluated. It is shown that simply treating the quality measures as DEA outputs does not help in discriminating the performance. Thus, the current study presents a new, more sensitive, quality-adjusted DEA (Q-DEA), which effectively deals with quality measures in benchmarking. We report the results of applying Q-DEA to a U.S. bank's 200-branch network that required a method for benchmarking to help manage operating costs and service quality. Q-DEA findings helped the bank achieve cost savings and improved operations while preserving service quality, a dimension critical to its mission. New insights about ways to improve branch operations based on the best-practice (high-quality low-cost) benchmarks identified with Q-DEA are also described in the paper. This demonstrates the practical need and potential benefits of Q-DEA and its efficacy in one application, and also suggests the need for further research on measuring and incorporating quality into DEA benchmarking. The review process of this paper was handled by the Edit-in-Chief Peter Hammer.     

Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction

Within the data envelopment analysis context, problems of discrimination between efficient and inefficient decision-making units often arise, particularly if there are a relatively large number of variables with respect to observations. This paper applies Monte Carlo simulation to generalize and compare two discrimination improving methods; principal component analysis applied to data envelopment analysis (PCA–DEA) and variable reduction based on partial covariance (VR). Performance criteria are based on the percentage of observations incorrectly classified; efficient decision-making units mistakenly defined as inefficient and inefficient units defined as efficient. A trade-off was observed with both methods improving discrimination by reducing the probability of the latter error at the expense of a small increase in the probability of the former error. A comparison of the methodologies demonstrates that PCA–DEA provides a more powerful tool than VR with consistently more accurate results. PCA–DEA is applied to all basic DEA models and guidelines for its application are presented in order to minimize misclassification and prove particularly useful when analyzing relatively small datasets, removing the need for additional preference information.     

Assessing the cost-effectiveness of university academic recruitment and promotion policies,

This paper develops an approach for higher education institutions to assess the economic efficiency of their recruitment and promotion practices concerning academic staff. Research output potential is a key criterion in most academic appointments. Generally, there is a long lead time between the conduct of research and its ultimate value in the form of disseminated knowledge. This means higher education institutions usually reward financially staff on the prospect of research output, albeit on the basis of research outputs achieved up to the point of recruitment or discretionary salary rise (e.g. through promotion). We propose a Data Envelopment Analysis (DEA) model which can be used retrospectively to set salary costs against corresponding research outputs achieved as a measure of the financial efficacy of past recruitment and promotion practices. The analysis can identify potential issues with those practices and lead to improvements for the future.     

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios.     

An MC2 linear programming approach to combined forecasting

Combined forecasting is a well-known forecasting technique of handling multiple forecasting methods. However, the previous combined forecasting approaches lack of incorporating the various possible opinions from several experts on an given forecasting problem. This paper proposes an MC² linear programming approach to determining weighted coefficients of combined forecasting that involves multiple experts. The numerical example of the paper shows that the proposed approach likely outperforms the current techniques of combined forecasting in dealing with the case of multiple experts.     

Impact of the industry on franchise chain efficiency: a meta-DEA and context-dependent DEA approach

A meta-DEA (Data Envelopment Analysis) approach is used to analyse the impact of the industry in which the franchisor operates its business on its chain efficiency by comparing chain efficiency between and across industries (retail versus services). To provide realistic short-term and long-term objectives to inefficient chains, a level-by-level benchmarking path is developed with the context-dependent DEA approach. Furthermore, the joint implementation of both approaches enables an identification of chains for which the industry influences their efficiency, an evaluation of the efficiency share that results from industry-specific factors rather than factors common to the franchise sector as a whole, and the characterization of an implementable and achievable benchmarking defining level-by-level improvements of chain efficiency and taking into account chain characteristics. Such an analysis can help franchisors to define their future strategic and managerial orientations.     

A study on the persistence of Farrell’s efficiency measure under a dynamic framework

This article introduces a sequence of four systematic methods to examine the extent to which the economic efficiency of Taiwan’s commercial banks persists and to uncover the potential dynamic link between bank performance and various financial indicators. Quasi-fixed inputs are explicitly incorporated in the DEA model to account for possible adjustment costs, regulation, or indivisibilities. Among the four methods, the dynamic panel data model and the Markov model appear to be exploited for the first time in the area of the DEA approach. Evidence is found that bank efficiency exhibits moderate persistence over the sample period, implying that the given sample banks fail to adjust their production techniques in a timely manner. Regulatory authorities and bank managers are suggested to be aware of the level of undesirable non-performing loans due to their close relationship with bank performance.     

基于DEA-Malmquist和Tobit regression的物联网上市公司效率评价

物联网的发展已经成为我国实现经济转型的有力支撑,但其发展过程中呈现的波动性问题仍亟需深入研究.从效率的角度,对我国19家物联网公司的发展状况进行了研究.首先,运用DEA模型测算了19家公司2013-2015年的技术效率、纯技术效率、规模效率以及各公司的规模收益状况.计算结果表明物联网上市公司的效率在整体上呈现上升趋势,只有同方股份在三年内规模收益状况为递减.其次,运用DEA-Malmquist测算了19家公司的技术效率变化指数、技术进步指数、纯技术效率变化指数、规模效率变化指数和全要素生产率变化指数.结果表明,技术进步指数低是制约这19家公司生产力发展的关键问题.最后,运用Tobit regression分析了11项财务指标对技术效率、纯技术效率以及规模效率影响的显著性,得出财务指标对规模效率存在显著影响.基于效率和影响因素分析的实际,为物联网上市公司的发展提供合理的建议,推动我国物联网产业实现良性有序发展.     

Measuring overall efficiency and effectiveness using DEA

This paper presents a framework where data envelopment analysis (DEA) is used to measure overall efficiency and show how to apply this framework to assess effectiveness for more general behavioral goals. The relationships between various cone-ratio DEA models and models to measure overall efficiency are clarified. Specifically it is shown that as multiplier cones tighten, the cone-ratio DEA models converge to measures of overall efficiency. Furthermore, it is argued that multiplier cone and cone-ratio model selection must be consistent with the behavioral goals assigned or assumed for purposes of analysis. Consistent with this reasoning, two new models are introduced to measure effectiveness when value measures are represented by separable or linked cones, where the latter can be used to analyze profit-maximizing effectiveness.     

基于相关性分析与DEA模型的寿险公司效率分析

选取国内较有影响力的八家寿险公司作为研究对象,提出了基于相关性分析的DEA组合评价方法,该方法综合了相关性分析和DEA两种方法的优点.利用相关性分析的方法设计出评价寿险公司经营效率的投入和产出指标.然后综合运用DEA模型对这八家公司的经营效率进行研究,分析影响效率有效性的因素.     

A dynamic benchmarking system for assessing the recovery of inpatients: Evidence from the neurorehabilitation process

The shortage of medical resources (mainly beds) is a critical and increasingly prevalent problem affecting hospitals. Of the factors that contribute to these shortages, the ambiguity and insufficiency of the criteria used to identify whether an inpatient should be discharged are among the most detrimental. To address this issue, this study applies data envelopment analysis (DEA) on existing inpatient data from the Neurorehabilitation Center at Toronto’s Bridgepoint Hospital to create a dynamic benchmarking system to evaluate the health stage of an inpatient ready to be discharged. Unlike the more traditional parametric techniques, DEA provides non-subjective benchmarking that does not require any prior specification of the production function making it a more desirable choice for this application. The dynamic model categorizes the inpatient’s discharge status as rejected, under observation, or approved. This new approach not only allows managers to gain insight into the potential causes of medical resource shortages, but also allows clinicians to treat inpatients more effectively based on their discharge categories. For validation, the results of the dynamic model were compared with actual inpatient discharge assessments provided by the Bridgepoint Hospital.     

Sustainable workforce scheduling in construction program management

The multimode resource-constrained project scheduling problem (MRCPSP) deals with the scheduling of a set of projects with alternative requirements of renewable and non-renewable resources. Solutions to the MRCPSP usually consider objectives in terms of cost and time. However, social objectives related with the workforce may impact the performance of projects and affect program sustainability goals. To account for this new social input, this paper extends the MRCPSP and proposes a new multiobjective mixed-integer programming model. The proposed solution method uses an a priori lexicographic ordering of the objectives, followed by an ?-constraints approach. The model is illustrated with a case study of a construction program.     

Data envelopment analysis application in sustainability: The origins,development and future directions

Sustainable development and sustainability assessment have been of great interest to both academe and practitioners in the past decades. In this study, we review the literature on data envelopment analysis (DEA) applications in sustainability using citation-based approaches. A directional network is constructed based on citation relationships among DEA papers published in journals indexed by the Web of Science database from 1996 to March 2016. We first draw the citation chronological graph to present a complete picture of literature development trajectory since 1996. Then we identify the local main DEA development paths in sustainability research by assigning an importance index, namely search path count (SPC), to each link in the citation network. The local main path suggests that the current key route of DEA applications in sustainability focus on the environmental sustainability. Through the Kamada–Kawai layout algorithm, we find four research clusters in the literature including corporate sustainability assessment, regional sustainability assessment, sustainability composite indicator construction, and sustainability performance analysis. For each of the clusters, we further identify the key articles based on citation network and local citation scores, demonstrate the developmental trajectory of the literature, and suggest future research directions.     

Measuring income inequalities beyond the Gini coefficient

Growing interest in the analysis of interrelationships between income distribution and economic growth has recently stimulated new theoretical and empirical research. Measures such as the head-count ratio for the poverty index or the widely used Gini coefficient are aggregated indicators describing the general extent of inequality without deeper insights into income distribution among households. To derive an indicator accounting for income distribution among income groups, we propose a new approach based on an output oriented DEA model where the input value is unitized to 1 for each country and weights restrictions imposed so as to favour a higher income share in the lower quantiles. We demonstrate the merits of this approach on the quintile income breakdown data of 29 European countries. Prioritizing lower income groups’ welfare, countries such as Slovenia and Slovakia can be equally favoured by the new proposed indicator while being assessed differently by the Gini index. An intertemporal analysis reveals a slight deterioration of income distribution in the majority of 29 European countries over the period of 2007–2016 in a Rawlsian sense.